AI Agent is emerging as a crucial application form for the commercial implementation of large AI models, evolving from a technical tool into a new factor of production. The industry is entering a critical phase of large-scale adoption, with investment enthusiasm running high in this sector. In August 2025, the State Council's "Opinions on Deepening the Implementation of the 'AI Plus' Initiative" set a target for the penetration rate of next-generation intelligent agent applications in China to exceed 90% by 2030. In February 2026, eight departments including the Ministry of Industry and Information Technology, the Cyberspace Administration of China, and the National Development and Reform Commission jointly released the "Implementation Opinions on the Special Action for 'AI Plus Manufacturing'," proposing goals to promote the deep application of 3-5 general large models in manufacturing by 2027 and launch 1,000 high-level industrial intelligent agents.

AI Agent emphasizes autonomy and goal orientation, serving as a vital application for the commercial deployment of large models. An AI Agent primarily refers to an intelligent entity capable of perceiving its environment, autonomously planning, making decisions, and executing actions to achieve objectives. According to the International Organization for Standardization (ISO), an AI Agent can use sensors to perceive its environment and respond via effectors, possessing autonomy and authority. As defined by Gartner, an AI Agent is an autonomous or semi-autonomous software entity that utilizes artificial intelligence technologies to perceive, make decisions, take actions, and achieve business or personal goals in digital and physical environments. Compared to traditional AI tools, AI Agents emphasize characteristics such as autonomy, interactivity, reactivity, and adaptability, offering advantages including high autonomy, strong execution capabilities, handling of complex tasks, continuous learning, and real-time interaction abilities.

Within the Agent's "perception-decision-action" closed-loop system, large AI models empower the core reasoning and knowledge hub of AI Agents, providing general capabilities like dialogue, logical reasoning, content generation, and code comprehension. They are key technologies driving Agent intelligence but lack autonomous action capabilities themselves. Existing enterprise business systems, such as CRM, provide specific application scenarios and execution targets for AI Agent systems, offering perceptual data and feedback loops for value realization. In the future, these three components will integrate more deeply: large AI models will continue to evolve as the "intelligent foundation," AI Agents will become new "digital employees" for enterprises, connecting various business subsystems, and existing enterprise business systems will become increasingly intelligent, automated, and personalized through Agent-oriented transformation.

Based on different technical architectures, AI Agents can be categorized into five core types: reactive agents, model-based agents, goal-based agents, utility-based agents, and learning agents. Among them, reactive agents respond directly based on condition-action rules, have low implementation costs, and lack memory capacity. They are primarily used in scenarios like industrial automation control and simple customer service robots, while also providing foundational support for complex agent systems in IoT contexts. Goal-based agents decompose complex goals into executable sub-tasks, achieving multi-objective balance and optimization through path planning and dynamic adjustment, mainly applied in project management and marketing campaign optimization. Learning agents represent the highest form of agent technology, possessing the ability to learn from experience and self-improve, representing a key technical path towards true autonomous intelligence. They are primarily used in scenarios like intelligent customer service evolution, dynamic pricing systems, and personalized recommendations, though they still face challenges such as cold-start problems, sample efficiency, and safety assurance.

Top-level policies explicitly support the widespread adoption of next-generation intelligent agent applications, propelling rapid growth in the AI Agent market. The AI Agent industry chain is divided from top to bottom into the foundational layer, platform layer, and application layer. The upstream foundational layer mainly includes essential core technologies like base large models, knowledge enhancement, and vector databases. The midstream platform layer primarily consists of agent development frameworks, orchestration platforms, and general and specialized agents. The downstream application layer mainly includes enterprise-level applications, consumer-level applications, and general productivity tools. Vertical industry domains for enterprise applications primarily include finance, telecommunications, energy, and healthcare, while consumer application scenarios mainly encompass personal assistants, educational tutoring, and lifestyle services. Additionally, the downstream sector also includes numerous general productivity tools like code assistants, writing assistants, and data analysis assistants.

In August 2025, the State Council's "Opinions on Deepening the Implementation of the 'AI Plus' Initiative" clearly stated the goal for China's next-generation intelligent agent application penetration rate to exceed 90% by 2030. This indicates that AI Agents are no longer limited to efficiency tools but are becoming new factors of production that can drive business growth, suggesting vast future market potential. Driven by a combination of factors including national policy impetus, breakthroughs in core technologies, validation of value at scale, and deep capital investment, the AI Agent industry is experiencing rapid development with explosive growth in application scenarios. Market forecasts from multiple authoritative institutions suggest that the AI Agent field will maintain rapid growth over the next five years. According to Markets and Markets, the global AI Agent market size is projected to grow from $5.1 billion in 2024 to $47.1 billion by 2030, representing a compound annual growth rate (CAGR) of 44.8%. Gartner predicts that the proportion of task-specific AI agents integrated into enterprise applications will rise from less than 5% in 2025 to 40% by the end of 2026.

The AI Agent field features numerous types of players, with increasingly complex market competition and diversified business models. Market participants in the AI Agent domain are varied, primarily including: 1) AI-native platform or framework providers, represented by companies like LangChain, CrewAI, Dify; 2) Technology and cloud service giants, represented by companies like Alibaba Cloud, Tencent Cloud, Google, Microsoft, Volcano Engine; 3) Large model manufacturers, represented by companies like OpenAI, Anthropic, Zhipu AI; 4) Vertical industry solution providers, represented by companies like Minglue Technology, Deep Empowerment Intelligence, Yiliang Technology; 5) Traditional enterprise transformers, represented by companies like Yonyou Network, iFlytek, Kingdee.

Currently, competition in the AI Agent market is becoming more complex. AI-native platform or framework providers mainly offer development platforms, frameworks, or underlying algorithms, characterized by technical leadership and strong generality. Technology and cloud service giants build full-stack capabilities based on the "model + cloud + ecosystem" approach, positioning agents as ecosystem entry points. Their advantages lie in extremely strong capabilities in technology, computing power, data, and ecosystem integration, providing one-stop solutions. Large model manufacturers are entering the fray, attempting to embed some agent capabilities directly into the model layer. Vertical industry solution providers deeply cultivate specific sectors, integrating AI Agents closely with business processes to address specific pain points. Their advantages include profound industry know-how, highly targeted products, and the ability to deliver value quickly. Traditional enterprise transformers integrate AI Agent capabilities into existing mature product lines to achieve intelligent upgrades. Their strengths lie in vast existing customer bases and deep business understanding, enabling smooth empowerment of current systems.

Currently, mainstream business models in the AI Agent field include SaaS subscription models, platform ecosystem models, and enterprise customization services. Among these, the SaaS subscription model typically manifests as out-of-the-box applications, characterized by low cost, rapid deployment, and the advantage of lowering the barrier to AI adoption. The platform ecosystem model primarily takes the form of Agent development platforms or app stores, enabling empowerment and connectivity while building ecosystem barriers, offering significant potential value. Enterprise customization services often involve privately deployed dedicated Agent systems deeply integrated with customer business processes, characterized by high fit, high controllability, and the ability to address the most complex core business problems.

AI Agents possess diverse working paradigms and can penetrate a wide range of industries. Currently, AI Agents have been implemented in services across multiple sectors including media and entertainment, travel and hospitality, customer support, and financial services. Their commercial value is becoming increasingly prominent, especially in application scenarios like customer service, code development, marketing, and data analysis. For instance, in customer service scenarios, AI Agents can significantly reduce response times and improve issue resolution rates through conversational understanding, rapidly penetrating industries like e-commerce and banking. In software development, AI Agents contribute through code generation, code review, and documentation generation, reportedly helping GitHub Copilot users increase coding speed by an average of 55% and reduce bug rates by 15-20%. In data analysis, AI Agents enable functionalities like natural language querying, automated report generation, and anomaly detection, with penetration rates rising quickly in retail and corporate financial management.

Furthermore, different types of Agents often need to collaborate within the same application scenario. For example, in a smart factory, basic reactive agents might handle equipment safety monitoring and emergency shutdowns; model-based agents could perform equipment status prediction and maintenance recommendations; goal-based agents might formulate production plans and resource scheduling; utility-based agents would optimize overall operational efficiency and cost control; and finally, learning agents would continuously optimize process parameters from production data. Consequently, enterprises implementing AI Agents typically follow an implementation path from "basic capability building -> intelligent upgrade -> value optimization -> adaptive evolution," progressing from simple to complex and from localized to comprehensive.

Simultaneously, agent applications are accelerating their shift from general-purpose to vertical specialization. Demand for agent types shows distinct variation across different industries. For example, manufacturing focuses on production efficiency and quality control, emphasizing agent applications with predictive capabilities and process optimization goals. The financial industry prioritizes risk control and personalized services, leaning towards utility-optimizing and continuous learning agents. Healthcare emphasizes diagnostic accuracy and treatment efficacy, primarily utilizing goal-based and learning agents. Additionally, with the maturation of edge computing technology, agents are increasingly deployed at the edge, often using lightweight reactive or model-based architectures capable of providing real-time responsiveness in resource-constrained environments.

Investment activity in the AI Agent sector has increased significantly since 2025, with frequent large financing rounds. In April 2025, Manus secured $75 million in funding led by U.S. venture firm Benchmark. In September 2025, Anthropic raised $13 billion in a Series F round from institutions including Iconiq Capital and Lightspeed Venture Partners. In January 2026, Step星辰 received a 5 billion yuan Series B+ investment from numerous institutions including Shanghai Guotou, China Life Equity, and Pudong Venture Capital. The sector has also witnessed several mergers and acquisitions. For instance, Meta acquired the representative general Agent company Manus for billions of dollars; Amazon acquired general artificial intelligence firm Adept to quickly strengthen its R&D capabilities in AGI and Agents; Beisen acquired training Agent provider Kurang Technology for 180 million yuan; OPPO strategically acquired writing Agent provider Waveform Intelligence.

Investment stages in the AI Agent field are primarily concentrated in the early and growth phases. Investment focus is expanding from general platforms to specialized Agents in vertical industries, with numerous investment events occurring in specialized agent domains like finance, sales, legal, code, and customer service. Companies with rich data, clearly defined problems, and high value realization potential in vertical sectors are particularly attractive to capital. Investment institutions prioritize agent companies that already have paying customers and positive cash flow. Concurrently, investment attention has noticeably increased for upstream core technology foundations, with rising investment heat in sub-sectors like Agent frameworks and orchestration, machine learning security, and observability and governance.

Currently, the commercial implementation of AI Agents still faces challenges and difficulties in areas such as technical capabilities, economic viability, ecosystem, and security. Technically, AI Agents currently primarily empower single-point tasks, have limited ability to integrate complex business processes, and suffer from stability and success rate issues when executing long task chains. Furthermore, general large models have limited capability in handling professional business tasks. Commercially, there exists a situation where general Agents do not address pain points deeply enough, while enterprise-level Agents, though high in value, face high implementation barriers. Ecologically, integration with internal enterprise systems is difficult; standard protocols like MCP are often insufficient for complex enterprise systems; and there is a strong dependency on external large model manufacturers, posing risks of rising model costs or supply disruption. Security-wise, the autonomy introduced by AI Agents brings new security risks during processes like prompt injection and permission chaining, and related regulatory frameworks and compliance definitions need further refinement.

In response to these challenges, AI Agents are evolving towards Multi-Agent Systems, where different specialized agents communicate, coordinate, and cooperate to accomplish more complex tasks. This process will spur the development of new communication protocols, orchestration platforms, and governance frameworks. Simultaneously, the trend towards accelerated ecosystem synergy for AI Agents is evident. System architectures are evolving from monolithic to distributed agent networks, creating demand for AgentOps platforms. Through unified mechanisms for agent monitoring, scheduling, and optimization, these platforms will support enterprises in building autonomous and controllable agent networks. This will push AI Agent vendors to compete based on "technology foundation + ecosystem integration" capabilities rather than single products, potentially leading to further industry consolidation. Additionally, the business model for AI Agents is transitioning from subscription-based SaaS to Result as a Service (RaaS), deeply linking Agent value with actual enterprise business outcomes and driving the industry's upgrade from "selling tools" to "selling value."